Does Your Desk Look Like This

Extensive experiments we performed on a real-world dataset demonstrate that the defined features are effective to determine relevant target researchers. The proposed method generates more accurate recommendations for relevant researchers when compared to a Baseline method. Common Author Relations, Collaborative Filtering, Random Walk, Article Recommendation, Citation Recommendation. With the rapid emergence of big scholarly data, tremendous growth of knowledge is now largely captured in digital form and archived all over the world. Archival materials are also currently being digitized. Provided online to people for free or by paying a fee. Such situation creates the commonly known information overload problem especially in academia while bringing a significant advantage that allows people to easily access more knowledge. For example, a researcher in academia needs to find articles of interest to read for generating a research idea or citing an article related to the article he is writing, an author needs to submit his manuscript to a certain journal of which the topic is relevant to the manuscript, an editor needs to assign a manuscript to a reviewer who is an expert in the domain which the manuscript belongs to, or a researcher in a domain needs to collaborative with another researcher in another domain.

This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track. The key aspects of our system include: meaningful integration of various disease, gene, and drug name ontologies, training of a novel perceptron model for article relevance labeling, a ranking module that considers additional factors such as journal impact and article publication year, and comprehensive query matching rules. Experimental results demonstrate that our proposed system considerably outperforms the results of the best participating system of the TREC 2017 precision medicine challenge. Providing useful precision medicine (PM)-related information to clinicians treating cancer patients is in high demand. For example, given a case with patient’s disease (type of cancer), the relevant genetic variants (genes, mutations, alterations), basic demographic information, and other potential factors that may be relevant, the clinician may want to find out biomedical articles addressing relevant treatments for the given patient.

All Sentences CNN - the input to the CNN model is a claim followed by the first 100 sentences of the article, without any detection of their relevance. The articles with higher number of sentences are clipped. Those with lower number of sentences are padded with zero vector. This network is meant for comparison purposes, to determine the effect of sentence relevance detection. Attention LSTM - this model uses an LSTM network for obtaining high-level representations for both the claim and the article body. An attention mechanism is applied on the high-level representations to identify important parts of the articles. Another LSTM layer is applied on the output of the attention layer. A dropout with rate of 0.4 is applied to prevent overfitting, followed by a dense layer and a softmax layer for classification. Similarity CNN - the input to the CNN model are the three most similar sentences to a given claim (based on the cosine similarity of their embedding representations) along with one previous and one following sentence for each.

In Section V, we evaluate the proposed approach and compared it with the baseline methods. Section VI presents the conclusion. Future work of the paper. In this section, we present existing work related to the problem of citations count prediction. We can classify the existing research works for the citations count prediction (CCP) based on academic features (Section II-A), graph of the scientific papers (Section II-B), post-publication contents of the article (Section II-C), and the hybrid model based CCP (Section II-D). Academic features such as authors’ features, link-based features, and posterior features form part of the first category of information used in citations counts. ’s features, including the hub value, the authority’s importance, the number of co-authors, the number of articles published and the number of citations. Then they considered the number, average, and maximum value of the features of authors as characteristics of the paper. The authors of this article found that the author’s information in an article will help in predicting the published paper’s future citations count.

POSTSUBSCRIPT which need mathematical treatment. In this article, we review the theory of the system of kinematical conservation laws (KCL), which govern the evolution of these surfaces and have been developed by the second author and his collaborators. POSTSUBSCRIPT with physically realistic singularities. POSTSUBSCRIPT are discontinuous. The main aim of this article is to identify density of the conserved variable and the flux for the KCL which we did not do earlier. The presentation of this article is like that in a popular article, which which aims at non-experts in the field. POSTSUBSCRIPT is called a ray. POSTSUBSCRIPT as seen in Figure 1, if formed by ray theory, disappears. Both the ray theory and the level set theory to solve the eikonal equation are inadequate to study formation and evolution of a kink. POSTSUBSCRIPT, i.e. the eikonal equation and ray theory breakdown, then we need to go to the more basic formulation for the evolution of a curve or surface, which is KCL.